UP TO 50% OFF: Efficient Implementation of Polynomial Masking

While passive probing attacks and active fault attacks have been studied for multiple decades, research has only started to consider combined attacks that use both probes and faults relatively recently. During this period, polynomial masking became a promising, provably secure countermeasure to protect cryptographic computations against such combined attacks. Unlike other countermeasures, such as duplicated additive masking, polynomial masking can be implemented using a linear number of shares, as shown by Berndt et al. at CRYPTO ’23. Based upon this fact, Arnold et al. noted at CHES ’24 that polynomial masking is particularly well-suited for parallel computation. This characteristic is especially effective in scenarios involving multiple circuits with identical structures, such as the 16 SBoxes in AES. Just recently, Faust et al. showed at CHES ’25 that one can also incorporate the technique of packed secret sharing into these masking schemes, given that the state-of-the-art polynomial masking scheme is secure against combined attacks.In this work, we present provably secure advancements regarding this state-of-the-art scheme in both computational and randomness efficiency, reducing the randomness complexity by up to 50% and the computational complexity even more by going from a quadratic term to a linear one for many parameters. Moreover, we present the first implementation of a polynomial masking scheme against combined attacks along with an extensive experimental evaluation for a wide range of parameters and configurations as well as a statistical leakage detection to evaluate the security of the implementation on an Arm Cortex-M processor. Our implementation is publicly available to encourage further research in practical combined resilience.